docs(readme): change repo name

README.md

@@ -2632,8 +2632,8 @@ input_texts = ['query: how much protein should a female eat',
                "passage: As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
                "passage: Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."]
 
-tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-small-v2')
-model = AutoModel.from_pretrained('intfloat/e5-small-v2')
+tokenizer = AutoTokenizer.from_pretrained('ggrn/e5-small-v2')
+model = AutoModel.from_pretrained('ggrn/e5-small-v2')
 
 # Tokenize the input texts
 batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
@@ -2679,6 +2679,6 @@ Below is an example for usage with sentence_transformers. `pip install sentence_
 This is community contributed, and results may vary up to numerical precision.
 ```python
 from sentence_transformers import SentenceTransformer
-model = SentenceTransformer('intfloat/e5-small-v2')
+model = SentenceTransformer('ggrn/e5-small-v2')
 embeddings = model.encode(input_texts, normalize_embeddings=True)
 ```